Page 129 - AJWEP-v22i3

Groundwater trends in Karakalpakstan

The first half of the dataset, referred to as the upper The arrangement of straight lines within the 12-point
n polygon reveals the annual variation pattern and allows
series, comprises the years n 1 2 3 ,, , , , while the
2 for location-specific qualitative interpretations. Each
second half, known as the lower series, includes the side of the polygon assumes a linear transition between
years n + 1, n + 2 ,…, n. Implementing the IPTA months. Applying this linearity assumption to shorter
2 2 periods enhances the accuracy of trend analyses.
method on both monthly and seasonal scales involves a
multi-step procedure, utilizing a predefined framework If the slopes of all connecting lines are similar and
with 12 dispersion points to represent each month of the the polygon edges align in a consistent direction, the
year. The model incorporates the calculation of arithmetic resulting polygon resembles a narrow band close
mean and standard deviation (SD) values for the dataset. to a global regression line. This indicates uniform,
By analyzing successive months, the IPTA model isotropic variation in the hydrometeorological variable.
identifies trends, allowing for the determination of Conversely, wider polygons suggest greater temporal
slopes, lengths, and associated values that characterize variability.
these monthly transitions. Subsequently, trend polylines A polygon with a rising orientation generally suggests
are generated by connecting successive segments of the balanced hydro-climatic conditions. However, the
time series, forming the foundation for further analysis. appearance of multiple polygons or internal loops may
This graphical representation facilitates both numerical occur under certain circumstances, reflecting dynamic
and qualitative interpretations of the examined time and complex variability. Horizontal intersections with
series, offering deeper insights into the climatic the polygon represent the expected range of monthly
variations captured in the data. variations, whereas vertical intersections indicate the
The calculation procedure for the ITPA model is as magnitude and limits of hydrometeorological quantities.
follows: A smaller polygon area reflects consistent monthly
(i) The dataset of size n is divided into two equal parts precipitation and stable hydrological events, while a
for comprehensive analysis. reduced overall slope from the horizontal axis indicates
(ii) The mean and SD are computed for each month in higher-intensity hydrometeorological phenomena.
both subsets. Ultimately, the IPTA model serves as a valuable tool in
(iii) The first series is plotted on the horizontal axis water resource planning, operation, and management,
of a scatter chart, whereas the second series is enabling systematic evaluation of both quantitative and
represented on the vertical axis. qualitative properties of hydrometeorological dynamics.
(iv) Points representing consecutive months are
connected using straight lines to form a polygon. 3. Results and discussion
(v) The slope and length of each connecting line
are calculated using Equations VII and VIII, 3.1. Serial correlation analysis
respectively. Before applying the MK and SR tests to detect trends in
the groundwater time series, it was necessary to assess
y y
S 2 1 (VII) the presence of serial autocorrelation in the data. To this
x x end, lag-1 autocorrelation coefficients (r ) were
2 1 1
calculated and evaluated for statistical significance
x
L ( x ) 2 ( y y ) 2 (VIII) under the null hypothesis at the 95% confidence level
2
1
2
1
The IPTA model template for monthly records using a two-tailed test: r 196. N .
1
offers a framework for both qualitative and quantitative
analyses of hydrometeorological systems, yielding The autocorrelation coefficients for the monthly
several key insights. First, the straight line connecting groundwater level data across various provinces were
2 consecutive months represents changes in monthly computed, with the results summarized in Table 2. The
values, whereas the closed polygon indicates the natural analysis revealed that all calculated serial correlation
balance of the system over a year. The length of each coefficients remained below their respective critical
line illustrates the magnitude of monthly variations. thresholds. This indicates that the monthly groundwater
When the slopes of these lines are relatively consistent level time series do not exhibit significant serial
in both vertical and horizontal directions, it suggests dependence. Consequently, the MK and SR trend
minimal monthly contributions to the overall variation. tests were applied directly to the full datasets without

Volume 22 Issue 3 (2025) 123 doi: 10.36922/AJWEP025080052